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Introduction to Formal Preference Spaces

Poznaniu, 2005. In Polish. [11] Konrad Raczkowski and Paweł Sadowski. Equivalence relations and classes of abstraction. Formalized Mathematics , 1( 3 ):441-444, 1990. [12] George F. Schumm. Transitivity, preference, and indifference. Philosophical Studies , 52: 435-437, 1987. [13] Andrzej Trybulec. Domains and their Cartesian products. Formalized Mathematics , 1( 1 ): 115-122, 1990. [14] Andrzej Trybulec. Enumerated sets. Formalized Mathematics , 1( 1 ):25-34, 1990. [15] Wojciech A

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Son Preference, Parity Progression and Contraceptive Use in South Asia

References Arnold, F. (1997). Gender preferences for children. Demographic and Health Surveys Comparative Studies, no.23. Arnold, F. (2001). Son preference in South Asia. In: Z.A. Sathar & J.F. Phillips (Eds.) Fertility Transition in South Asia . Oxford University Press. Basu, D. & De Jong, R. (2010). Son targeting fertility behavior: Some consequences and determinants. Demography, 47(2): 521-536. Bhat, P.M. & Zavier, A.F. (2003). Fertility decline and gender bias in Northern India. Demography, 40(4): 637-657. Bongaarts, J

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E-Commerce Customers’ Preference Implicit Identification

web user interest with implicit indicators , Master Thesis, Florida Institute of Technology, USA. Kelly, D. (2005). Implicit Feedback: Using Behavior to Infer Relevance. New directions in cognitive information retrieval, The Information Retrieval Series, Vol. 19, Section IV. Kelly, D. & Belkin, N.J. (2001). Reading time, scrolling and interaction: exploring implicit sources of user preferences for relevance feedback . In SIGIR ’01. Kim, H. & Chan, P.K. (2008). Implicit Indicators For Interesting Web Pages

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The pattern of facial preferences in boys at early adolescence

menstrual cycle phase on face preferences , Arch. Sex. Behav., 37 , 78-84 Jones B. C., A. C. Little, I. S. Penton-Voak, B. P. Tiddeman, D. M. Burt, D. I. Perrett, 2001, Facial symmetry and judgements of apparent health. Support for a "good genes" explanation of the attractiveness-symmetry relationship , Evol. Hum. Behav., 22 , 417-29 Kissler J., K. H. Bäuml, 2000, Effects of the beholder's age on the perception of facial attractiveness . Acta Psychol, 104 , 145-66 Kościński K., 2007

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Identifyingy the Utility Function of Transport Services From Stated Preferences

(4):404-412, doi: 10.3846/16484142.2013.867282 5. Green, P. E., Srinivasan, V. (1978) Conjoint analysis in consumer research: Issues and outlook. Journal of Consumer Research, 5, 103-123. 6. Heinitz, F., Fritzlar, E. (2014) Reconstructing Surveyed Itineraries and Choices between Inter-City and Regional Train Services, Period. Polytech. Transp. Eng., (42)2:111-117, doi: 10.3311/PPtr.7465 7. Karajz, S. (2009) Közgazdasági elméletek (Economical theories), tutorial notes 8. Kok, R. (2013) New Car Preferences Move Away

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Food Preference of Oryzaephilus Surinamensis (Coleoptera: Silvanidae) to Different Types of Plant Products

different types of chocolate varying in quantity of cocoa” Bulletin of Insectology, Vol. 69 (1), 21-24, 2016. [6] L. Astuti, M.B. Mario, and T. Widjayanti, “Preference, growth and development of Oryzaephilus surinamensis (L.) (Coleoptera: Silvanidae) on red, white and black rice in whole grain and flour form”, Journal of Entomological Research, Vol. 42 (4), 461-468, 2018. [7] S.A. Zulaikha, M. Halim, A.R. Nor Atikah, and S. Yaakop, “Diversity and abundance of storage pest in rice warehouses in Klang, Selangor, Malaysia”, Serangga, Vol. 23 (1), 89–98, 2018

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Habit Formation and Preference Change with Capital and Renewable Resources

-Hill. 8. Becker, G. S. (1992), „Habits, addictions and traditions“, Kyklos, Vol. 45, No. 3, pp. 327-45. 9. Becker, G.S., Mulligan, C.B. (1997), „The endogenous determination of time preference“, The Quarterly Journal of Economics, Vol. 112, No. 3, pp. 729-58. 10. Beltratti, A., Chichilnisky, G., and Heal, G.M. (1994), „Sustainable Growth and the Golden Rule“, in Goldin, I., Winters, I.A. (Eds.) The Economics of Sustainable Development, Cambridge University Press, Cambridge. 11. Blanchard, O.J., Fischer, S. (1989) Lectures

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Understanding Consumer Preferences from Social Media Data

Abstract

Consumers produce enormous amounts of textual data of product reviews online. Artificial intelligence (AI) can help analyze this data and generate insights about consumer preferences and decision-making. A GfK research project tested how we can use AI to learn consumer preferences and predict choices from publicly available social media and review data. The common AI tool “Word Embeddings” was used and has shown to be a powerful way to analyze the words people use. It helped reveal consumers’ preferred brands, favorite features and main benefits. Language biases uncovered by the analysis can indicate preferences. Compared to actual sales data from GfK panels, they fit reasonably within various categories. Especially when data volumes were large, the method produced very accurate results. By using free, widespread online data it is completely passive, without affecting respondents or leading them into ranking or answering questions they would otherwise not even have thought of. The analysis is fast to run and no fancy processing power is needed.

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Preference Measurement with Conjoint Analysis. Overview of State-of-the-Art Approaches and Recent Developments

References Eggers, F. and Sattler, H. (2009), “Hybrid Individualized Two-level Choice-based Conjoint (HIT-CBC): A New Method for Measuring Preference Structures with Many Attribute Levels, International Journal of Research in Marketing”, 26 (2), pp. 108 - 118. Huber, J. and Zwerina, K. (1996), “The Importance of Utility Balance in Efficient Choice Designs, Journal of Marketing Research”, 33(3), pp. 307 - 317. Louviere, J.J. and G. Woodworth (1983), “Design and Analysis of Simulated Consumer Choice or Allocation

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Assessment preferences and learning styles in ESP

Abstract

The article deals with the research on assessment preferences reflected in learning styles within English for Specific Purposes (ESP) instruction on the higher education level. The sample group consisted of 287 respondents of the Faculty of Informatics and Management, University of Hradec Kralove, Czech Republic. The main objective of the research was to discover expected correlations between respondents’ learning styles and relating preferences in selected assessment formats. Two questionnaires were applied to reach the objective; however, the expectations did not prove. The discovered findings were discussed within the world context.

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